Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation

TL;DR

This paper introduces SERec, a novel social recommendation approach that models user exposure to items via social information rather than shared preferences, improving recommendation accuracy.

Contribution

The paper proposes a new method to incorporate social exposure into collaborative filtering, challenging the assumption of preference similarity among friends.

Findings

SERec outperforms state-of-the-art methods on four datasets.

Social regularization and social boosting are effective in modeling social exposure.

The methods are robust and scalable for real-world applications.

Abstract

This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar preferences with their social friends. Which, however, may not hold true due to various motivations of making online friends and dynamics of online social networks. Inspired by recent causal process based recommendations that first model user exposures towards items and then use these exposures to guide rating prediction, we utilize social information to capture user exposures rather than user preferences. We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality. Under this new assumption, in this paper, we present a novel recommendation approach (named SERec) to integrate social exposure into collaborative filtering. We propose two methods to implement SERec, namely social regularization and social boosting, each with different ways to construct social exposures. Experiments on four real-world datasets demonstrate that our methods outperform the state-of-the-art methods on top-N recommendations. Further study compares the robustness and scalability of the two proposed methods.